Review: If you’re looking for a machine learning laptop, Jack Wallen is confident you won’t find a better option on the market than the Lambda Tensorbook.
Every so often a new technology comes along that changes the landscape of how businesses operate, developers develop, and data is used and manipulated. machine learning is such a technology.
Machine learning is a branch of artificial intelligence which focuses on using data and algorithms to imitate the way people learn to gradually improve accuracy, ability, and interaction. Machine learning can be applied to use cases such as:
- Voice assistants
- Personalized Marketing
- Fraud detection
- Autonomous vehicles
- Optimize transportation
- Behavioral prediction
- Process automation
The thing about machine learning is that it takes a lot of hardware to do the job correctly. Run a machine learning platform or app on the wrong hardware and it will bring that machine to a halt faster than you can point your finger at the video screen and the ‘Make it like this’ command.
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Run that platform or app on the appropriate hardware and it will run at warp speed to its destination.
So when I got the . received RAZER Lambda Tensor book to review, I was excited to see which category it would fall under.
I had a sneaking suspicion that it would be capable of warp speed right out of the gate.
I was not wrong.
What is the Lambda Tensor Book?
Simply put, the Lambda Tensorbook is a laptop designed specifically for machine learning. It’s a brilliant combination of hardware and software that come together to create a seriously impressive platform that allows ML developers to develop and test without running out of energy.
The Tensorbook starts with a GeForce RTX 3080 Max-Q 16GB GPU that can reportedly deliver model training up to 4x faster than even the Apple M1 Max and up to 10x faster than Google Colab instances.
Then comes the full Lambda stackwhich comes pre-installed on Ubuntu Linux and includes:
- The latest NVIDIA drivers
- tensor current
Lambda Stack is used by Apple, Intel, Samsung, IBM, Microsoft, Amazon, Adobe, LinkedIn, Boeing, Harvard and even the US Department of Defense.
The remaining hardware list looks like this:
- VRAM–16GB GDDR6
- CPU Intel Core i7-11800H
- RAM–64GB 3200MHz DDR4
- Storage – 2TB NVMe PCIe 4.0
- Display–165Hz 1440p 15.6″
- 2 Thunderbolt 4 (USB-C)
- HDMI 2.1
- lock lock
- UHS-III SD Card Reader
- 3 USB 3.2 Gen 2 Type A
- 3.5mm Headphone/Microphone Combo Jack
- Power Port
Other hardware features include:
- Wi-Fi 6E
- Bluetooth 5.2
- 80 Watt Hour LiPro Battery
- Dimensions-35.5 cm x 23.5 cm x 1.69 cm
The Lambda Tensorbook’s weight comes in at a solid 4.43 pounds, but this isn’t a laptop exactly meant to be extremely portable. It’s heavy, but that weight brings some serious power with it.
Speaking of which…
The pure power of the Lambda Tensorbook
One really cool thing about the Lambda Tensorbook review unit is that it came installed with a pretty cool ML application called DeepFaceLive† This application description said it is a real-time face swap for PC streaming or video calling. I won’t go into how this type of application can be abused, but it’s seriously impressive to see it work.
To launch the application I had to open a terminal window, change the ~/DeepFaceLive directory and issue the command:
python3 main.py run DeepFaceLive --userdata-dir ./data
When the app opens (Image A), it uses the laptop’s camera to stream your face swapped with one chosen from the Model drop-down list.
I was shocked at how smooth the streaming face swap video looked. Out of the box, it is set up to use the NVIDIA GeForce GPU as a computing device. After switching to the CPU, the streaming video became choppy and the laptop slowed down dramatically. Switch the device back to GPU (from the Device dropdown in the Face Swap section) and performance will be back to normal.
Another example application is a Jupyter Notebook created for benchmarking. Running this app will open a new notebook in the default Firefox browser, where you can browse the different sections to see benchmark information (Figure B) based on different training models.
Nice details in the Lambda Tensorbook
There are some really nice touches to be found in the Lambda Tensorbook that have less to do with machine learning and more with just making a great laptop. First, the keyboard is fantastic. It comes with purple backlight keys (Figure C) that have just the right travel and feel for those who need a responsive keyboard.
Next, the speakers on this thing are some of the best sounding I’ve heard on a laptop in a while. Compared to my MacBook Pro M1, the Lambda Tensorbook is a joy to listen to.
The case for this laptop is also very sleek (Figure D) with clean lines and little detraction from a spartan and functional design. The case reminds me of my favorite Pixel Chromebook from 2015.
Finally, the desktop is a pretty bare-bones GNOME that has a dark theme by default. While I prefer lighter themes, I can see why they’d want to go with the dark option, given that battery life will be a first when you run this beast off of a wall outlet.
Who is the Lambda Tensorbook for?
If I’ve ever experienced a more niche piece of hardware, I can’t remember what it was. The Lambda Tensorbook is niche at its best…and I mean that in all the positive permutations of the sentence. This laptop is an absolute beast that will shrug off anything you throw at it (especially if you’re doing ML training with the GPU). So if machine learning and AI are your problem (and you’re willing to pay the $3,499 for the base model), this laptop is what you want.
It’s expensive, it’s heavy and it can get pretty hot, but if you need unparalleled mobile power for ML/AI, you’d be hard pressed to find a better option.
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